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E-raamat: Spatial Capture-Recapture

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(Research Statistician, U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA), (North Carol), (Warnell School of Forestry and Natural Resources, Athens, GA, USA), (North Carolina State University, Raleigh, NC, USA)
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  • Ilmumisaeg: 27-Aug-2013
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780124071520
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Spatial Capture-RecaptureSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Aug-2013
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780124071520
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"Space plays a vital role in virtually all ecological processes (Tilman and Kareiva, 1997; Hanski, 1999; Clobert et al., 2001). The spatial arrangement of habitat can influence movement patterns during dispersal, habitat selection, and survival. The distance between an organism and its competitors and prey can influence activity patterns and foraging behavior. Further, understanding distribution and spatial variation in abundance is necessary in the conservation and management of populations"--

Spatial Capture-Recapture provides a comprehensive how-to manual with detailed examples of spatial capture-recapture models based on current technology and knowledge. Spatial Capture-Recapture provides you with an extensive step-by-step analysis of many data sets using different software implementations. The authors' approach is practical – it embraces Bayesian and classical inference strategies to give the reader different options to get the job done. In addition, Spatial Capture-Recapture provides data sets, sample code and computing scripts in an R package.

  • Comprehensive reference on revolutionary new methods in ecology makes this the first and only book on the topic
  • Every methodological element has a detailed worked example with a code template, allowing you to learn by example
  • Includes an R package that contains all computer code and data sets on companion website

Arvustused

"...a book for the DIY quantitative ecologist who wants to understand their data...I enjoyed it tremendously and it already had a strong influence on how I think about some of my current research projects." --Basic and Applied Ecology

"...a timely and informative contribution that summarizes the history and motivation behind SCR models,...will be a vital addition to wildlife ecologists book shelves for many years to come." --The Journal of Wildlife Management, Sep 14

Muu info

Spatial Capture-Recapture provides a revolutionary extension of traditional capture-recapture methods for studying animal populations using data from live trapping, camera trapping, DNA sampling, acoustic sampling, and related field methods.
Foreword xvii
Preface xxiii
Acknowledgments xxix
PART I BACKGROUND AND CONCEPTS
Chapter 1 Introduction
3(18)
1.1 The Study of Populations by Capture-Recapture
4(1)
1.2 Lions and Tigers and Bears, oh my: Genesis of Spatial Capture-Recapture Data
5(3)
1.2.1 Camera trapping
5(1)
1.2.2 DNA sampling
6(1)
1.2.3 Acoustic sampling
7(1)
1.2.4 Search-encounter methods
7(1)
1.3 Capture-Recapture for Modeling Encounter Probability
8(4)
1.3.1 Example: Fort Drum bear study
8(3)
1.3.2 Inadequacy of non-spatial capture-recapture
11(1)
1.4 Historical Context: A Brief Synopsis
12(2)
1.4.1 Buffering
12(1)
1.4.2 Temporary emigration
13(1)
1.5 Extension of Closed Population Models
14(3)
1.5.1 Toward spatial explicitness: Efford's formulation
15(1)
1.5.2 Abundance as the aggregation of a point process
15(1)
1.5.3 The activity center concept
16(1)
1.5.4 The state-space
16(1)
1.5.5 Abundance and density
17(1)
1.6 Ecological Focus of SCR Models
17(1)
1.7 Summary and Outlook
18(3)
Chapter 2 Statistical Models and SCR
21(26)
2.1 Random Variables and Probability Distributions
22(5)
2.1.1 Stochasticity in ecology
22(2)
2.1.2 Properties of probability distributions
24(3)
2.2 Common Probability Distributions
27(7)
2.2.1 The binomial distribution
27(2)
2.2.2 The Bernoulli distribution
29(1)
2.2.3 The multinomial and categorical distributions
30(1)
2.2.4 The Poisson distribution
31(1)
2.2.5 The uniform distribution
32(1)
2.2.6 Other distributions
33(1)
2.3 Statistical Inference and Parameter Estimation
34(3)
2.4 Joint, Marginal, and Conditional Distributions
37(3)
2.5 Hierarchical Models and Inference
40(1)
2.6 Characterization of SCR Models
41(4)
2.7 Summary and Outlook
45(2)
Chapter 3 GLMs and Bayesian Analysis
47(40)
3.1 GLMs and GLMMs
48(2)
3.2 Bayesian Analysis
50(6)
3.2.1 B ayes' rule
50(1)
3.2.2 Principles of Bayesian inference
51(2)
3.2.3 Prior distributions
53(1)
3.2.4 Posterior inference
54(1)
3.2.5 Small sample inference
55(1)
3.3 Characterizing Posterior Distributions by MCMC Simulation
56(4)
3.3.1 What goes on under the MCMC hood
57(2)
3.3.2 Rules for constructing full conditional distributions
59(1)
3.3.3 Metropolis-Hastings algorithm
59(1)
3.4 Bayesian Analysis Using the BUGS Language
60(3)
3.4.1 Linear regression in WinBUGS
61(2)
3.5 Practical Bayesian Analysis and MCMC
63(6)
3.5.1 Choice of prior distributions
63(2)
3.5.2 Convergence and so forth
65(3)
3.5.3 Bayesian confidence intervals
68(1)
3.5.4 Estimating functions of parameters
68(1)
3.6 Poisson GLMs
69(6)
3.6.1 North American breeding bird survey data
69(2)
3.6.2 Poisson GLM in WinBUGS
71(1)
3.6.3 Constructing your own MCMC algorithm
72(3)
3.7 Poisson GLM with Random Effects
75(2)
3.8 Binomial GLMs
77(3)
3.8.1 Binomial regression
78(1)
3.8.2 North American waterfowl banding data
79(1)
3.9 Bayesian Model Checking and Selection
80(4)
3.9.1 Goodness-of-fit
81(2)
3.9.2 Model selection
83(1)
3.10 Summary and Outlook
84(3)
Chapter 4 Closed Population Models
87(38)
4.1 The Simplest Closed Population Model: Model M0
88(4)
4.1.1 The core capture-recapture assumptions
90(1)
4.1.2 Conditional likelihood
91(1)
4.2 Data Augmentation
92(9)
4.2.1 DA links occupancy models and closed population models
93(2)
4.2.2 Model M0 in BUGS
95(2)
4.2.3 Remarks on data augmentation
97(1)
4.2.4 Example: Black bear study on Fort Drum
98(3)
4.3 Temporally Varying and Behavioral Effects
101(1)
4.4 Models with Individual Heterogeneity
102(6)
4.4.1 Analysis of model Mh
104(1)
4.4.2 Analysis of the Fort Drum data with model Mh
105(2)
4.4.3 Comparison with MLE
107(1)
4.5 Individual Covariate Models: Toward Spatial Capture-Recapture
108(8)
4.5.1 Example: Location of capture as a covariate
109(1)
4.5.2 Example: Fort Drum black bear study
110(2)
4.5.3 Extension of the model
112(3)
4.5.4 Invariance of density to B
115(1)
4.5.5 Toward fully spatial capture-recapture models
115(1)
4.6 Distance Sampling: A Primitive SCR Model
116(4)
4.6.1 Example: Sonoran desert tortoise study
118(2)
4.7 Summary and Outlook
120(5)
PART II BASIC SCR MODELS
Chapter 5 Fully Spatial Capture-Recapture Models
125(46)
5.1 Sampling Design and Data Structure
126(1)
5.2 The Binomial Observation Model
126(3)
5.2.1 Definition of home range center
129(1)
5.2.2 Distance as a latent variable
129(1)
5.3 The Binomial Point Process Model
129(5)
5.3.1 The state-space of the point process
131(2)
5.3.2 Connection to model Mh and distance sampling
133(1)
5.4 The Implied Model of Space Usage
134(5)
5.4.1 Bivariate normal case
136(1)
5.4.2 Calculating space usage
136(2)
5.4.3 Relevance of understanding space usage
138(1)
5.4.4 Contamination due to behavioral response
138(1)
5.5 Simulating SCR Data
139(2)
5.5.1 Formatting and manipulating data sets
140(1)
5.6 Fitting Model SCRO in BUGS
141(2)
5.7 Unknown N
143(7)
5.7.1 Analysis using data augmentation in WinBUGS
145(2)
5.7.2 Implied home range area
147(1)
5.7.3 Realized and expected population size
148(2)
5.8 The Core SCR Assumptions
150(1)
5.9 Wolverine Camera Trapping Study
151(7)
5.9.1 Practical data organization
151(3)
5.9.2 Fitting the model in WinBUGS
154(2)
5.9.3 Summary of the wolverine analysis
156(1)
5.9.4 Wolverine space usage
157(1)
5.10 Using a Discrete Habitat Mask
158(4)
5.10.1 Evaluation of coarseness of habitat mask
159(2)
5.10.2 Analysis of the wolverine camera trapping data
161(1)
5.11 Summarizing Density and Activity Center Locations
162(5)
5.11.1 Constructing density maps
162(2)
5.11.2 Wolverine density map
164(2)
5.11.3 Predicting where an individual lives
166(1)
5.12 Effective Sample Area
167(2)
5.13 Summary and Outlook
169(2)
Chapter 6 Likelihood Analysis of Spatial Capture-Recapture Models
171(26)
6.1 MLE with Known N
171(6)
6.1.1 Implementation (simulated data)
173(4)
6.2 MLE When N is Unknown
177(4)
6.2.1 Integrated likelihood under data augmentation
180(1)
6.2.2 Extensions
180(1)
6.3 Classical Model Selection and Assessment
181(1)
6.4 Likelihood Analysis of the Wolverine Camera Trapping Data
182(4)
6.4.1 Sensitivity to integration grid and state-space buffer
183(1)
6.4.2 Using a habitat mask (restricted state-space)
184(2)
6.5 DENSITY and the R Package secr
186(10)
6.5.1 Encounter device types and detection models
188(1)
6.5.2 Analysis using the secr package
189(2)
6.5.3 Likelihood analysis in the secr package
191(2)
6.5.4 Multi-session models in secr
193(1)
6.5.5 Some additional capabilities of secr
194(2)
6.6 Summary and Outlook
196(1)
Chapter 7 Modeling Variation in Encounter Probability
197(22)
7.1 Encounter Probability Models
198(5)
7.1.1 Bayesian analysis with bear.JAGS
200(1)
7.1.2 Bayesian analysis of encounter probability models
200(3)
7.2 Modeling Covariate Effects
203(8)
7.2.1 Date and time
204(2)
7.2.2 Trap-specific covariates
206(1)
7.2.3 Behavior or trap response by individual
207(1)
7.2.4 Individual covariates
208(3)
7.3 Individual Heterogeneity
211(2)
7.3.1 Models of heterogeneity
212(1)
7.3.2 Heterogeneity induced by variation in home range size
212(1)
7.4 Likelihood Analysis in secr
213(4)
7.4.1 Notes for fitting standard models
214(1)
7.4.2 Sex effects
215(1)
7.4.3 Individual heterogeneity
216(1)
7.4.4 Model selection in secr using AIC
216(1)
7.5 Summary and Outlook
217(2)
Chapter 8 Model Selection and Assessment
219(26)
8.1 Model Selection by AIC
220(4)
8.1.1 AIC analysis of the wolverine data
220(4)
8.2 Bayesian Model Selection
224(8)
8.2.1 Model selection by DIC
225(1)
8.2.2 DIC analysis of the wolverine data
225(2)
8.2.3 Bayesian model-averaging with indicator variables
227(4)
8.2.4 Choosing among detection functions
231(1)
8.3 Evaluating Goodness-of-Fit
232(1)
8.4 The Two Components of Model Fit
233(8)
8.4.1 Testing uniformity or spatial randomness
234(3)
8.4.2 Assessing fit of the observation model
237(1)
8.4.3 Does the SCR model fit the wolverine data?
238(3)
8.5 Quantifying Lack-of-Fit and Remediation
241(1)
8.6 Summary and Outlook
242(3)
Chapter 9 Alternative Observation Models
245(32)
9.1 Poisson Observation Model
245(9)
9.1.1 Poisson model of space usage
247(1)
9.1.2 Poisson relationship to the Bernoulli model
248(1)
9.1.3 A cautionary note on modeling encounter frequencies
249(1)
9.1.4 Analysis of the Poisson SCR model in BUGS
250(1)
9.1.5 Simulating data and fitting the model
251(2)
9.1.6 Analysis of the wolverine study data
253(1)
9.1.7 Count detector models in the secr package
253(1)
9.2 Independent Multinomial Observations
254(12)
9.2.1 Multinomial resource selection models
256(1)
9.2.2 Simulating data and analysis using JAGS
256(3)
9.2.3 Multinomial relationship to the Poisson
259(1)
9.2.4 Avian mist-netting example
260(6)
9.3 Single-Catch Traps
266(4)
9.3.1 Inference for single-catch systems
267(1)
9.3.2 Analysis of Efford's possum trapping data
268(2)
9.4 Acoustic Sampling
270(4)
9.4.1 The signal strength model
272(1)
9.4.2 Implementation in secr
273(1)
9.4.3 Implementation in BUGS
273(1)
9.4.4 Other types of acoustic data
274(1)
9.5 Summary and Outlook
274(3)
Chapter 10 Sampling Design
277(30)
10.1 General Considerations
278(3)
10.1.1 Model-based not design-based
278(1)
10.1.2 Sampling space or sampling individuals?
279(1)
10.1.3 Focal population vs. state-space
280(1)
10.2 Study Design for (Spatial) Capture-Recapture
281(2)
10.3 Trap Spacing and Array Size Relative to Animal Movement
283(4)
10.3.1 Black bears from Pictured Rocks National Lakeshore
286(1)
10.4 Sampling Over Large Areas
287(2)
10.5 Model-Based Spatial Design
289(10)
10.5.1 Statement of the design problem
290(2)
10.5.2 Model-based design for SCR
292(1)
10.5.3 An optimal design criterion for SCR
293(1)
10.5.4 Too much math for a Sunday afternoon
294(2)
10.5.5 Optimization of the criterion
296(2)
10.5.6 Illustration
298(1)
10.5.7 Density covariate models
299(1)
10.6 Temporal Aspects of Study Design
299(3)
10.6.1 Total sampling duration and population closure
300(1)
10.6.2 Diagnosing and dealing with lack of closure
301(1)
10.7 Summary and Outlook
302(5)
PART III ADVANCED SCR MODELS
Chapter 11 Modeling Spatial Variation in Density
307(22)
11.1 Homogeneous Point Process Revisited
308(3)
11.2 Inhomogeneous Point Processes
311(3)
11.3 Observed Point Processes
314(4)
11.4 Fitting Inhomogeneous Point Process SCR Models
318(4)
11.4.1 Continuous space
318(2)
11.4.2 Discrete space
320(2)
11.5 Argentina Jaguar Study
322(4)
11.6 Summary and Outlook
326(3)
Chapter 12 Modeling Landscape Connectivity
329(20)
12.1 Shortcomings of Euclidean Distance Models
330(1)
12.2 Least-cost Path Distance
331(4)
12.2.1 Example of computing cost-weighted distance
333(2)
12.3 Simulating SCR Data Using Ecological Distance
335(3)
12.4 Likelihood Analysis of Ecological Distance Models
338(1)
12.4.1 Example of SCR with least-cost path
338(1)
12.5 Bayesian Analysis
339(1)
12.6 Simulation Evaluation of the MLE
339(2)
12.7 Distance in an Irregular Patch
341(4)
12.7.1 Basic geographic analysis in R
341(4)
12.8 Ecological Distance and Density Covariates
345(1)
12.9 Summary and Outlook
345(4)
Chapter 13 Integrating Resource Selection with Spatial Capture-Recapture Models
349(16)
13.1 A Model of Space Usage
350(4)
13.1.1 A simulated example
352(1)
13.1.2 Poisson model of space use
353(1)
13.2 Integrating Capture-Recapture Data
354(1)
13.3 SW New York Black Bear Study
355(4)
13.4 Simulation Study
359(2)
13.5 Relevance and Relaxation of Assumptions
361(1)
13.6 Summary and Outlook
362(3)
Chapter 14 Stratified Populations: Multi-Session and Multi-Site Data
365(16)
14.1 Stratified Data Structure
366(1)
14.2 Multinomial Abundance Models
367(5)
14.2.1 Implementation in BUGS
368(1)
14.2.2 Groups with no individuals observed
369(1)
14.2.3 The group-means model
370(1)
14.2.4 Simulating stratified capture-recapture data
371(1)
14.3 Other Approaches to Multi-Session Models
372(1)
14.4 Application to Spatial Capture-Recapture
372(5)
14.4.1 Multinomial ("multi-catch") observations
373(1)
14.4.2 Reanalysis of the ovenbird data
374(3)
14.5 Spatial or Temporal Dependence
377(1)
14.6 Summary and Outlook
378(3)
Chapter 15 Models for Search-Encounter Data
381(20)
15.1 Search-Encounter Designs
382(1)
15.1.1 Design 1: Fixed search path
382(1)
15.1.2 Design 2: Uniform search intensity
383(1)
15.2 A Model for Fixed Search Path Data
383(7)
15.2.1 Modeling total hazard to encounter
384(2)
15.2.2 Modeling movement outcomes
386(1)
15.2.3 Simulation and analysis in JAGS
386(3)
15.2.4 Hard plot boundaries
389(1)
15.2.5 Analysis of other protocols
390(1)
15.3 Unstructured Spatial Surveys
390(2)
15.3.1 Mountain lions in Montana
391(1)
15.3.2 Sierra National Forest fisher study
392(1)
15.4 Design 2: Uniform Search Intensity
392(6)
15.4.1 Alternative movement models
394(1)
15.4.2 Simulating and fitting uniform search models
395(2)
15.4.3 Movement and dispersal in open populations
397(1)
15.5 Partial Information Designs
398(1)
15.6 Summary and Outlook
398(3)
Chapter 16 Open Population Models
401(32)
16.1 Background
402(2)
16.1.1 Brief overview of population dynamics
402(1)
16.1.2 Animal movement related to population demography
403(1)
16.2 Jolly-Seber Models
404(11)
16.2.1 Traditional Jolly-Seber models
404(3)
16.2.2 Data augmentation for the Jolly-Seber model
407(4)
16.2.3 Spatial Jolly-Seber models
411(4)
16.3 Cormack-Jolly-Seber Models
415(11)
16.3.1 Traditional CJS models
415(3)
16.3.2 Multi-state CJS models
418(4)
16.3.3 Spatial CJS models
422(4)
16.4 Modeling Movement and Dispersal Dynamics
426(3)
16.4.1 Cautionary note
427(1)
16.4.2 Thoughts on movement of American shad
427(1)
16.4.3 Modeling dispersal
428(1)
16.5 Summary and Outlook
429(4)
PART IV SUPER-ADVANCED SCR MODELS
Chapter 17 Developing Markov Chain Monte Carlo Samplers
433(40)
17.1 Why Build Your Own MCMC Algorithm?
433(1)
17.2 MCMC and Posterior Distributions
434(2)
17.3 Types of MCMC Sampling
436(13)
17.3.1 Gibbs sampling
436(5)
17.3.2 Metropolis-Hastings sampling
441(3)
17.3.3 Metropolis-within-Gibbs
444(4)
17.3.4 Rejection sampling and slice sampling
448(1)
17.4 MCMC for Closed Capture-Recapture Model Mh
449(3)
17.5 MCMC Algorithm for Model SCRO
452(4)
17.5.1 SCR model with binomial encounter process
455(1)
17.6 Looking at Model Output
456(6)
17.6.1 Markov chain time series plots
457(1)
17.6.2 Posterior density plots
457(1)
17.6.3 Serial autocorrelation and effective sample size
458(2)
17.6.4 Summary results
460(1)
17.6.5 Other useful commands
461(1)
17.7 Manipulating the State-Space
462(3)
17.8 Increasing Computational Speed
465(6)
17.8.1 Parallel computing
465(3)
17.8.2 Using C++
468(3)
17.9 Summary and Outlook
471(2)
Chapter 18 Unmarked Populations
473(24)
18.1 Existing Models for Inference About Density in Unmarked Populations
474(2)
18.2 Spatial Correlation in Count Data
476(2)
18.2.1 Spatial correlation as information
476(1)
18.2.2 Types of spatial correlation
477(1)
18.3 Spatial Count Model
478(3)
18.3.1 Data
478(1)
18.3.2 Model
478(2)
18.3.3 On N being unknown
480(1)
18.3.4 Inference
481(1)
18.4 How Much Correlation is Enough?
481(1)
18.5 Applications
482(7)
18.5.1 Simulation example
482(6)
18.5.2 Northern parula in Maryland
488(1)
18.6 Extensions of the Spatial Count Model
489(5)
18.6.1 Improving precision
489(2)
18.6.2 Dusky salamanders in Maryland
491(3)
18.7 Summary and Outlook
494(3)
Chapter 19 Spatial Mark-Resight Models
497(30)
19.1 Background
499(5)
19.1.1 Resighting techniques
499(1)
19.1.2 Types of mark-resighting data
499(1)
19.1.3 A short history of mark-resight models
500(2)
19.1.4 The random sample assumption
502(2)
19.2 Known Number of Marked Individuals
504(4)
19.2.1 Implementing spatial mark-resight models
505(3)
19.3 Unknown Number of Marked Individuals
508(4)
19.3.1 Canada geese in North Carolina
509(3)
19.4 Imperfect Identification of Marked Individuals
512(2)
19.5 How Much Information Do Marked and Unmarked Individuals Contribute?
514(2)
19.6 Incorporating Telemetry Data
516(5)
19.6.1 Raccoons on the Outer Banks of North Carolina
519(2)
19.7 Point Process Models for Marked Individuals
521(3)
19.7.1 Homogeneous point process in a subset of S
521(2)
19.7.2 Inhomogeneous point processes
523(1)
19.8 Summary and Outlook
524(3)
Chapter 20 2012: A Spatial Capture-Recapture Odyssey
527(12)
20.1 Emerging Topics
530(5)
20.1.1 Modeling territoriality
531(1)
20.1.2 Combining data from different surveys
531(1)
20.1.3 Misidentification
532(1)
20.1.4 Gregarious species
533(1)
20.1.5 Single-catch traps
534(1)
20.1.6 Model fit and selection
534(1)
20.1.7 Explicit movement models
534(1)
20.2 Final Remarks
535(4)
PART V APPENDIX
Appendix 1 Useful Softwares and R Packages
539(1)
20.3 WinBUGS
539(1)
20.3.1 WinBUGS through R
539(1)
20.4 OpenBUGS
540(1)
20.4.1 OpenBUGS through R
540(1)
20.5 JAGS
541(1)
20.5.1 JAGS through R
541(1)
20.6 R
542(3)
20.6.1 R Packages
542(3)
Bibliography 545(24)
Index 569
Dr Royle is a Senior Scientist and Research Statistician at the U.S. Geological Survey's Patuxent Wildlife Research Center. His research is focused on the application of probability and statistics to ecological problems, especially those related to animal sampling and demographic modeling. Much of his research over the last 10 years has been devoted to the development of methods illustrated in our new book. He has authored or coauthored more than 100 journal articles, and co-authored the books Spatial Capture Recapture, Hierarchical Modeling and Inference in Ecology and Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, all published by Academic Press.
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